Fixed-weight controller for multiple systems

We demonstrate here a perhaps unexpected result: the ability of a single fixed-weight time-lagged recurrent network, properly trained, to act as a stabilizing controller for multiple (here 3) distinct and unrelated systems, without explicit knowledge of system identity. This capability, which may be...

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Bibliographic Details
Published inProceedings of International Conference on Neural Networks (ICNN'97) Vol. 2; pp. 773 - 778 vol.2
Main Authors Feldkamp, L.A., Puskorius, G.V.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Adaptive systems
Automotive engineering
Control systems
Engines
Laboratories
Noise robustness
Robust control
Stability
Switches
Veins
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Summary:We demonstrate here a perhaps unexpected result: the ability of a single fixed-weight time-lagged recurrent network, properly trained, to act as a stabilizing controller for multiple (here 3) distinct and unrelated systems, without explicit knowledge of system identity. This capability, which may be regarded as a challenge to the usual understanding of what constitutes an adaptive system, seemed plausible to us on the basis of our earlier results on both multiple time-series prediction and robust controller training. We describe our training method, which has been enhanced toward enforcing stability of the closed-loop system and dealing with process noise, and provide some results.
ISBN:0780341228
9780780341227
DOI:10.1109/ICNN.1997.616120